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android / platform / external / armnn / 246bd463 / . / src / backends / backendsCommon / test / layerTests / Conv2dTestImpl.cpp
blob: b0b2981d8d4191f18a9a3a52c29bfa99df7b254a [file] [log] [blame]
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Conv2dTestImpl.hpp"
#include <QuantizeHelper.hpp>
#include <armnnUtils/TensorUtils.hpp>
#include <armnnUtils/DataLayoutIndexed.hpp>
#include <armnnUtils/Permute.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/test/DataLayoutUtils.hpp>
#include <backendsCommon/test/TensorCopyUtils.hpp>
#include <backendsCommon/test/WorkloadTestUtils.hpp>
#include <test/TensorHelpers.hpp>
#include <boost/numeric/conversion/cast.hpp>
#include <string>
//
// Static data
//
// 2-channel bias used by a number of Conv2d tests.
static std::vector<float> Bias2({0, 2});
static std::vector<float> Bias4({1, 2, 3, 4});
static std::vector<float> Bias8({1, 2, 3, 4, 1, 2, 3, 4});
// 3-channel 16x8 image used as common input data for a number of Conv2d tests.
static std::vector<float> ConvInput3x8x16({
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
});
using namespace armnnUtils;
//
// Helper templates
//
// Helper template that returns either Bias2 or an empty vector depending on whether bias is enabled.
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale)
{
if(biasEnabled)
{
armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias2.size())}, ArmnnType);
boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(Bias2, qScale, 0.0f));
return bias;
}
else
{
return boost::multi_array<T, 1>();
}
}
// Helper template that returns either Bias4 or an empty vector depending on whether bias is enabled.
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
boost::multi_array<T, 1> GetBias4(bool biasEnabled, float qScale)
{
if(biasEnabled)
{
armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias4.size())}, ArmnnType);
boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(Bias4, qScale, 0.0f));
return bias;
}
else
{
return boost::multi_array<T, 1>();
}
}
// Helper template that returns either Bias8 or an empty vector depending on whether bias is enabled.
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
boost::multi_array<T, 1> GetBias8(bool biasEnabled, float qScale)
{
if(biasEnabled)
{
armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias4.size())}, ArmnnType);
boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(Bias8, qScale, 0.0f));
return bias;
}
else
{
return boost::multi_array<T, 1>();
}
}
// Helper template that returns either Bias4 or an empty vector depending on whether bias is enabled.
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
boost::multi_array<T, 1> GetBias(bool biasEnabled, float qScale, armnn::TensorInfo outputInfo, armnn::DataLayout layout)
{
const armnnUtils::DataLayoutIndexed dataLayoutIndexed(layout);
const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
const unsigned int outputChannels = outputInfo.GetShape()[channelsIndex];
switch (outputChannels)
{
case 2:
default:
{
return GetBias2<ArmnnType>(biasEnabled, qScale);
}
case 4:
{
return GetBias4<ArmnnType>(biasEnabled, qScale);
}
case 8:
{
return GetBias8<ArmnnType>(biasEnabled, qScale);
}
}
}
//
// Implementation templates
//
// Mapping from input type to bias type for fully connected layers.
// float => float, uint8_t => int32_t
template<typename T>
struct FullyConnectedBiasTypeForInputType;
template<>
struct FullyConnectedBiasTypeForInputType<float>
{
using Type = float;
};
template<>
struct FullyConnectedBiasTypeForInputType<uint8_t>
{
using Type = int32_t;
};
// Modifies a std::vector in-place using a specified bias.
template<typename T, typename B>
void ApplyBias(std::vector<T>& v, float vScale, int32_t vOffset,
const std::vector<B>& bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)
{
BOOST_ASSERT_MSG((armnn::IsQuantizedType<T>() && vScale != 0.0f) || (!armnn::IsQuantizedType<T>()),
"Invalid type and parameter combination.");
BOOST_ASSERT_MSG((armnn::IsQuantizedType<B>() && bScale != 0.0f) || (!armnn::IsQuantizedType<B>()),
"Invalid type and parameter combination.");
// Note we need to dequantize and re-quantize the image value and the bias.
for (uint32_t i = 0; i < bias.size(); ++i)
{
float dBias = SelectiveDequantize(bias[i], bScale, bOffset);
for (uint32_t y = 0; y < h; ++y)
{
for (uint32_t x = 0; x < w; ++x)
{
uint32_t offset = (i * h + y) * w + x;
BOOST_ASSERT(offset < v.size());
T& outRef = v[offset];
float dOutput = SelectiveDequantize(outRef, vScale, vOffset);
outRef = SelectiveQuantize<T>(dOutput + dBias, vScale, vOffset);
}
}
}
}
//
// Convolution2d implementations
//
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const boost::multi_array<T, 4>& originalInput,
const boost::multi_array<T, 4>& originalKernel,
const boost::multi_array<B, 1>& bias,
const boost::multi_array<T, 4>& originalOutputExpected,
float qScale,
int32_t qOffset,
const armnn::DataLayout layout = armnn::DataLayout::NCHW,
uint32_t padLeft = 0,
uint32_t padTop = 0,
uint32_t padRight = 0,
uint32_t padBottom = 0,
uint32_t strideX = 1,
uint32_t strideY = 1,
uint32_t dilationX = 1,
uint32_t dilationY = 1)
{
boost::ignore_unused(memoryManager);
unsigned int inputHeight = boost::numeric_cast<unsigned int>(originalInput.shape()[2]);
unsigned int inputWidth = boost::numeric_cast<unsigned int>(originalInput.shape()[3]);
unsigned int inputChannels = boost::numeric_cast<unsigned int>(originalInput.shape()[1]);
unsigned int inputNum = boost::numeric_cast<unsigned int>(originalInput.shape()[0]);
unsigned int outputHeight = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[2]);
unsigned int outputWidth = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[3]);
unsigned int outputChannels = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[1]);
unsigned int outputNum = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[0]);
unsigned int kernelHeight = boost::numeric_cast<unsigned int>(originalKernel.shape()[2]);
unsigned int kernelWidth = boost::numeric_cast<unsigned int>(originalKernel.shape()[3]);
unsigned int kernelChannels = boost::numeric_cast<unsigned int>(originalKernel.shape()[1]);
unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(originalKernel.shape()[0]);
bool biasEnabled = bias.size() > 0;
// This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).
BOOST_ASSERT(inputNum == 1);
BOOST_ASSERT(outputNum == 1);
// If a bias is used, its size must equal the number of output channels.
BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
// Note these tensors will use two (identical) batches.
armnn::TensorInfo inputTensorInfo =
armnnUtils::GetTensorInfo(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo =
armnnUtils::GetTensorInfo(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
armnn::TensorInfo kernelDesc =
armnnUtils::GetTensorInfo(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout, ArmnnType);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
{
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
kernelDesc.SetQuantizationScale(qScale);
kernelDesc.SetQuantizationOffset(qOffset);
biasDesc.SetQuantizationScale(qScale*qScale);
biasDesc.SetQuantizationOffset(0);
}
LayerTestResult<T, 4> ret(outputTensorInfo);
// Construct input data - two batches of the same input image.
std::vector<T> inputImage;
inputImage.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);
std::vector<T> inputData;
inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());
inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());
// at this point if we require it permute the input data
const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(inputData.size());
armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(T));
inputData = tmp;
}
auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
std::vector<T> outputImage;
outputImage.assign(originalOutputExpected.data(),
originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);
// Apply bias to output image if it is enabled.
if(biasEnabled)
{
std::vector<T> biasV;
biasV.assign(bias.data(), bias.data() + outputChannels);
ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
outputWidth, outputHeight);
}
// Construct expected output data - two identical images.
std::vector<T> outputData;
outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
// at this point if we require it permute the expected output
if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(outputData.size());
armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data(), sizeof(T));
outputData = tmp;
}
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::Convolution2dQueueDescriptor data;
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
// Permute the kernel if necessary
boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
if (layout == armnn::DataLayout::NHWC)
{
armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data(), sizeof(T));
}
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
if(biasEnabled)
{
AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
}
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
data.m_Parameters.m_StrideX = strideX;
data.m_Parameters.m_StrideY = strideY;
data.m_Parameters.m_PadLeft = padLeft;
data.m_Parameters.m_PadRight = padRight;
data.m_Parameters.m_PadTop = padTop;
data.m_Parameters.m_PadBottom = padBottom;
data.m_Parameters.m_BiasEnabled = biasEnabled;
data.m_Parameters.m_DataLayout = layout;
data.m_Parameters.m_DilationX = dilationX;
data.m_Parameters.m_DilationY = dilationY;
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
ExecuteWorkload(*workload, memoryManager);
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
return ret;
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
LayerTestResult<T, 4> SimpleConvolution2dNhwcTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const boost::multi_array<T, 4>& input,
const boost::multi_array<T, 4>& kernel,
const boost::multi_array<B, 1>& bias,
const boost::multi_array<T, 4>& outputExpected,
const armnn::DataLayout dataLayout,
float qScale,
int32_t qOffset,
uint32_t padLeft = 1,
uint32_t padTop = 1,
uint32_t padRight = 1,
uint32_t padBottom = 1,
uint32_t strideX = 1,
uint32_t strideY = 1)
{
boost::ignore_unused(qScale, qOffset);
unsigned int inputNum = boost::numeric_cast<unsigned int>(input.shape()[0]);
unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[3]);
unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[1]);
unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[2]);
unsigned int kernelChanMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
unsigned int outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
bool biasEnabled = bias.size() > 0;
// Creates the tensors.
armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, ArmnnType);
armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},
ArmnnType);
armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, ArmnnType);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
// Construct the input data.
std::vector<T> inputData;
inputData.assign(input.data(), input.data() + inputHeight*inputWidth*inputChannels);
auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
// Construct the output data, with bias applied, as appropriate.
std::vector<T> outputData;
outputData.assign(outputExpected.data(), outputExpected.data() + outputHeight*outputWidth*outputChannels);
LayerTestResult<T, 4> ret(outputTensorInfo);
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
armnn::Convolution2dQueueDescriptor data;
data.m_Weight = &weightsTensor;
data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
data.m_Parameters.m_StrideX = strideX;
data.m_Parameters.m_StrideY = strideY;
data.m_Parameters.m_PadLeft = padLeft;
data.m_Parameters.m_PadRight = padRight;
data.m_Parameters.m_PadTop = padTop;
data.m_Parameters.m_PadBottom = padBottom;
data.m_Parameters.m_BiasEnabled = biasEnabled;
data.m_Parameters.m_DataLayout = dataLayout;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
ExecuteWorkload(*workload, memoryManager);
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
return ret;
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T,4> Convolution1dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled)
{
using B = armnn::ResolveType<ArmnnBType>;
// Until we have a specialist 1D convolution layer, we can fake one using
// 2D convolution with the final dimension set to 1.
// I don't anticipate this being particularly slow, given that convolution is implemented
// as a matrix multiplication, at which point dimension doesn't matter.
unsigned int batchSize = 1;
unsigned int inputChannels = 2;
unsigned int outputChannels = 3;
unsigned int inputSize = 5; // The 1D size (could view as 'width' or 'height').
unsigned int kernelSize = 3;
unsigned int padSize = 2;
unsigned int stride = 1;
unsigned int outputSize = 7; // (inputSize + 2 * padSize - kernelSize + 1) / stride.
armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, ArmnnType);
armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, ArmnnType);
armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, ArmnnType);
armnn::TensorInfo biasInfo({outputChannels}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
{
inputInfo.SetQuantizationScale(qScale);
inputInfo.SetQuantizationOffset(qOffset);
outputInfo.SetQuantizationScale(qScale);
outputInfo.SetQuantizationOffset(qOffset);
kernelInfo.SetQuantizationScale(qScale);
kernelInfo.SetQuantizationOffset(qOffset);
biasInfo.SetQuantizationScale(inputInfo.GetQuantizationScale()*kernelInfo.GetQuantizationScale());
biasInfo.SetQuantizationOffset(0);
}
std::vector<T> inputData = QuantizedVector<T>(
{
5.0f, -2.0f, 2.5f, 0.0f, 1.0f,
-3.0f, 3.2f, 5.0f, 2.0f, 3.0f,
},
inputInfo.GetQuantizationScale(),
inputInfo.GetQuantizationOffset());
std::vector<T> kernelData = QuantizedVector<T>(
{
1.0f, 0.0f, 0.0f,
0.0f, 2.0f, -1.5f,
0.0f, 0.0f, 0.0f,
0.2f, 0.2f, 0.2f,
0.5f, 0.0f, 0.5f,
0.0f, -1.0f, 0.0f
},
kernelInfo.GetQuantizationScale(),
kernelInfo.GetQuantizationOffset());
std::vector<B> biasData =
QuantizedVector<B>({ 1.0f, 0.0f, 0.0f }, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset());
std::vector<T> outputData = QuantizedVector<T>(
{
4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f,
-0.6f, -0.6f + 0.64f, -0.6f + 0.64f + 1.0f, 0.64f + 1.0f + 0.4f, 1.0f + 0.4f + 0.6f, 0.4f + 0.6f, 0.6f,
2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f
},
outputInfo.GetQuantizationScale(),
outputInfo.GetQuantizationOffset());
// Optionally apply bias to output image.
if(biasEnabled)
{
ApplyBias(outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(),
biasData, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(),
1, outputSize);
}
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo);
armnn::Convolution2dQueueDescriptor data;
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle weightsTensor(kernelInfo);
armnn::ScopedCpuTensorHandle biasTensor(biasInfo);
AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data());
AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
AddInputToWorkload(data, info, inputInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
data.m_Bias = &biasTensor;
data.m_Parameters.m_StrideX = 1;
data.m_Parameters.m_StrideY = stride;
data.m_Parameters.m_PadLeft = 0;
data.m_Parameters.m_PadRight = 0;
data.m_Parameters.m_PadTop = padSize;
data.m_Parameters.m_PadBottom = padSize;
data.m_Parameters.m_BiasEnabled = biasEnabled;
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputData.data());
ExecuteWorkload(*workload, memoryManager);
// Output
LayerTestResult<T,4> ret(outputInfo);
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
ret.outputExpected = MakeTensor<T, 4>(outputInfo, outputData);
return ret;
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled,
armnn::DataLayout dataLayout)
{
boost::ignore_unused(biasEnabled);
// Use common single-batch 5x5 image.
armnn::TensorInfo inputDesc({1, 3, 4, 1}, ArmnnType);
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc,
{
1, 5, 2, 3,
8, 7, 3, 6,
3, 3, 9, 1
});
// Use a 2-element batch of 3-channel 3x3 kernels.
armnn::TensorInfo kernelDesc({1, 3, 3, 1}, ArmnnType);
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, {
4, 5, 6,
0, 0, 0,
3, 2, 1
});
// Expected output is 1 batch of a 5x5 image.
armnn::TensorInfo outputDesc({1, 3, 4, 1}, ArmnnType);
const std::vector<float> outputData =
{
23, 41, 33, 21,
44, 65, 76, 52,
82, 85, 79, 42
};
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, outputData);
return SimpleConvolution2dNhwcTestImpl<ArmnnType, ArmnnType>(
workloadFactory,
memoryManager,
input,
kernel,
boost::multi_array<T, 1>(),
expectedOutput,
dataLayout,
qScale,
qOffset);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> SimpleConvolution2d3x3Stride2x2TestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled,
const armnn::DataLayout& dataLayout)
{
boost::ignore_unused(biasEnabled);
// Input is a single-batch, 1 channel, 5x5 image.
armnn::TensorInfo inputDesc({1, 5, 5, 1}, ArmnnType);
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc,
{
1, 5, 2, 3, 5,
8, 7, 3, 6, 3,
3, 3, 9, 1, 9,
4, 1, 8, 1, 3,
6, 8, 1, 9, 2
});
// Use a 3x3 kernel.
armnn::TensorInfo kernelDesc({1, 3, 3, 1}, ArmnnType);
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc,
{
4, 5, 6,
0, 0, 0,
3, 2, 1
});
// Expected output is a single-batch, 1 channel, 3x3 image.
armnn::TensorInfo outputDesc({1, 3, 3, 1}, ArmnnType);
const std::vector<T> outputData =
{
23, 33, 24,
91, 99, 48,
26, 50, 19
};
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, outputData);
uint32_t padLeft = 1;
uint32_t padTop = 1;
uint32_t padRight = 1;
uint32_t padBottom = 1;
uint32_t strideX = 2;
uint32_t strideY = 2;
return SimpleConvolution2dNhwcTestImpl<ArmnnType, ArmnnType>(
workloadFactory,
memoryManager,
input,
kernel,
boost::multi_array<T, 1>(),
expectedOutput,
dataLayout,
qScale,
qOffset,
padLeft,
padTop,
padRight,
padBottom,
strideX,
strideY);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled,
const armnn::DataLayout layout)
{
// Use common single-batch 3-channel 16x8 image.
armnn::TensorInfo inputDesc({1, 3, 8, 16}, ArmnnType);
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(ConvInput3x8x16, qScale, qOffset));
// Use a 2-element batch with 3-channel 3x5 kernels.
armnn::TensorInfo kernelDesc({2, 3, 5, 3}, ArmnnType);
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
QuantizedVector<T>({
1, 1, 1,
1, -1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
2, 2, 2,
2, 2, 2,
2, 2, 2,
2, 2, 2,
2, 2, 2,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0
},
qScale, qOffset)));
// Expected output is 2 batch elements of a 1-channel 14x4 image.
armnn::TensorInfo outputDesc({1, 2, 4, 14}, ArmnnType);
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
QuantizedVector<T>({
-24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24,
-25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25,
-23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,
-23.5f, -23.5f, -23.5f,
-23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,
-23.5f, -23.5f, -23.5f,
5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
},
qScale, qOffset)));
return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
input,
kernel,
GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
expectedOutput,
qScale,
qOffset,
layout);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled,
const armnn::DataLayout layout)
{
// Use a 3x3 kernel, which exercises ArmCompute's direct convolution path.
// Use common single-batch 3-channel 16x8 image.
armnn::TensorInfo inputDesc({1, 3, 8, 16}, ArmnnType);
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(ConvInput3x8x16, qScale, qOffset));
// Use a 2-element batch of 3-channel 3x3 kernels.
armnn::TensorInfo kernelDesc({2, 3, 3, 3}, ArmnnType);
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
QuantizedVector<T>({
1, 1, 1,
1, -1, 1,
1, 1, 1,
0, 0, 0,
0, 0, 0,
0, 0, 0,
2, 2, 2,
2, 2, 2,
2, 2, 2,
0, 0, 0,
0, 0, 0,
0, 0, 0,
1, 1, 1,
1, 1, 1,
1, 1, 1,
0, 0, 0,
0, 0, 0,
0, 0, 0
},
qScale, qOffset)));
// Expected output is 1 batch of a 2-channel 14x6 image.
armnn::TensorInfo outputDesc({1, 2, 6, 14}, ArmnnType);
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
QuantizedVector<T>({
-15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15,
-16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16,
-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
},
qScale, qOffset)));
return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
input,
kernel,
GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
expectedOutput,
qScale,
qOffset,
layout);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::DataLayout layout,
float qScale,
int32_t qOffset)
{
// Use a single-batch 1-channel 3x3 image as input.
armnn::TensorInfo inputDesc({1, 1, 3, 3}, ArmnnType);
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(
QuantizedVector<T>({
11,21,31,
12,22,32,
13,23,33
},
qScale, qOffset)));
// Use 1 batch of a 1-channel 2x2 kernel.
armnn::TensorInfo kernelDesc({1, 1, 2, 2}, ArmnnType);
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
QuantizedVector<T>({
-11,-21,
-12,-22,
},
qScale, qOffset)));
// Expected output is 1 batch of a 1-channel 6x8 image.
// Manually calculated like this:
//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]
//[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..]
//[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..]
//[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..]
//[-11*0 -21*13 -12*0 -22*0 ; -11*13 -21*23 -12*0 -22*0 ; -11*23 -21*33 -12*0 -22*0 ; -11*33 -21*0 -12*0 -22*0 ..]
//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]
//[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..]
armnn::TensorInfo outputDesc({1, 1, 8, 6}, ArmnnType);
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
QuantizedVector<T>({
0, 0, 0, 0, 0, 0,
-242, -594, -934, -372, 0, 0,
-495, -1190, -1850, -725, 0, 0,
-538, -1256, -1916, -748, 0, 0,
-273, -626, -946, -363, 0, 0,
0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
},
qScale, qOffset)));
return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
input,
kernel,
GetBias2<ArmnnBType>(false, qScale * qScale),
expectedOutput,
qScale,
qOffset,
layout,
1, // Padding left.
2, // Padding top.
3, // Padding right.
4); // Padding bottom.
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::DataLayout layout,
float qScale,
int32_t qOffset)
{
// Use a single-batch 1-channel 5x5 image as input.
armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, ArmnnType);
boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(
QuantizedVector<T>({
11,21,31,41,51,
12,22,32,42,52,
13,23,33,43,53,
14,24,34,44,54,
15,25,35,45,55,
}, qScale, qOffset)));
// Use 1 batch of a 1-channel 4x4 kernel.
armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, ArmnnType);
boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
QuantizedVector<T>({
-11,-21,-31,-41,
-12,-22,-32,-42,
-13,-23,-33,-43,
-14,-24,-34,-44,
},
qScale, qOffset)));
// Expected output is 1 batch of a 1-channel 5x5 image.
armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, ArmnnType);
std::vector<T> myVec(outputDesc.GetNumElements(), 0);
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
QuantizedVector<T>({
-7140, -10580, -13940, -9300, -5230,
-9590, -14120, -18520, -12290, -6860,
-9980, -14560, -18960, -12560, -7000,
-7518, -10904, -14144, -9318, -5152,
-5032, -7256, -9376, -6142, -3368,
},
qScale, qOffset)));
return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
input,
kernel,
GetBias2<ArmnnBType>(false, qScale * qScale),
expectedOutput,
qScale,
qOffset,
layout,
1, // Padding left.
1, // Padding top.
2, // Padding right.
2); // Padding bottom.
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> Convolution2d3x3DilationTestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const std::vector<float>& inputNoQuantizedValues,
armnn::TensorInfo& inputTensorInfo,
const std::vector<float>& kernelNoQuantizedValues,
armnn::TensorInfo& kernelTensorInfo,
const std::vector<float>& outputExpectedNoQuantizedValues,
armnn::TensorInfo& outputTensorInfo,
uint32_t dilationX,
uint32_t dilationY,
armnn::DataLayout layout = armnn::DataLayout::NCHW,
uint32_t padLeft = 0,
uint32_t padTop = 0,
uint32_t padRight = 0,
uint32_t padBottom = 0,
uint32_t strideX = 1,
uint32_t strideY = 1,
bool biasEnabled = false
)
{
float qScale;
int32_t qOffset;
switch (ArmnnType)
{
case armnn::DataType::QAsymmU8:
{
qScale = 0.1f;
qOffset = 128;
break;
}
case armnn::DataType::QSymmS16:
{
qScale = 0.1f;
qOffset = 0;
break;
}
case armnn::DataType::Float32:
default:
{
qScale = 0.f;
qOffset = 0;
break;
}
}
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
kernelTensorInfo.SetQuantizationScale(qScale);
kernelTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
auto input = MakeTensor<T, 4>(inputTensorInfo,
std::vector<T>(QuantizedVector<T>(inputNoQuantizedValues,
inputTensorInfo.GetQuantizationScale(),
inputTensorInfo.GetQuantizationOffset())));
auto kernel = MakeTensor<T, 4>(kernelTensorInfo,
std::vector<T>(QuantizedVector<T>(kernelNoQuantizedValues,
kernelTensorInfo.GetQuantizationScale(),
kernelTensorInfo.GetQuantizationOffset())));
auto expectedOutput =
MakeTensor<T, 4>(outputTensorInfo,
std::vector<T>(QuantizedVector<T>(outputExpectedNoQuantizedValues,
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset())));
return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
input,
kernel,
GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
expectedOutput,
qScale,
qOffset,
layout,
padLeft,
padTop,
padRight,
padBottom,
strideX,
strideY,
dilationX,
dilationY);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
LayerTestResult<T, 4> Convolution2d3x3Dilation3x3Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
armnn::TensorInfo inputTensorInfo({1, 1, 10, 10}, ArmnnType);
std::vector<float> inputNoQuantizedValues =
{
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0
};
armnn::TensorInfo kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);
std::vector<float> kernelNoQuantizedValues =
{
1, 2, 3,
4, 5, 6,
7, 8, 9
};
// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,
// therefore the output will be 4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1
armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);
std::vector<float> outputExpectedNoQuantizedValues =
{
6., 5., 5., 5.,
6., 5., 5., 5.,
6., 5., 5., 5.,
3., 2., 2., 2.
};
return Convolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
inputNoQuantizedValues,
inputTensorInfo,
kernelNoQuantizedValues,
kernelTensorInfo,
outputExpectedNoQuantizedValues,
outputTensorInfo,
3,
3,
layout,
biasEnabled);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
LayerTestResult<T, 4> Convolution2d2x3x3Dilation3x3Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
armnn::TensorInfo inputTensorInfo({1, 2, 10, 10}, ArmnnType);
std::vector<float> inputNoQuantizedValues =
{
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0
};
armnn::TensorInfo kernelTensorInfo({ 1, 2, 3, 3}, ArmnnType);
std::vector<float> kernelNoQuantizedValues =
{
1, 2, 3,
4, 5, 6,
7, 8, 9,
1, 2, 3,
4, 5, 6,
7, 8, 9
};
// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,
// therefore the output will be 4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1
armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);
std::vector<float> outputExpectedNoQuantizedValues =
{
12., 10., 10., 10.,
12., 10., 10., 10.,
12., 10., 10., 10.,
6., 4., 4., 4.
};
return Convolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
inputNoQuantizedValues,
inputTensorInfo,
kernelNoQuantizedValues,
kernelTensorInfo,
outputExpectedNoQuantizedValues,
outputTensorInfo,
3,
3,
layout,
biasEnabled);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
LayerTestResult<T, 4> Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test(
armnn::IWorkloadFactory &workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
armnn::TensorInfo inputTensorInfo({1, 1, 10, 10}, ArmnnType);
std::vector<float> inputNoQuantizedValues =
{
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1
};
armnn::TensorInfo kernelTensorInfo({ 1, 1, 2, 2}, ArmnnType);
std::vector<float> kernelNoQuantizedValues =
{
1, 2,
3, 4
};
// Since the dilation rate is 2 this will dilate the kernel to be like 3x3: d(K-1)+1 --> 2 x (2-1) + 1 = 3,
// therefore the output will be 4x4: (I − K + 2P)/S +1 => trunc ( (10 - 3 + 2x2 ) / 3 + 1 )
// where, dilation size = d = 2; kernel size = K = 2; input size = I = 10; padding size = P = 2; stride = S = 3
armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);
std::vector<float> outputExpectedNoQuantizedValues =
{
4, 7, 7, 3,
6, 10, 10, 4,
6, 10, 10, 4,
2, 3, 3, 1
};
uint32_t padLeft = 1;
uint32_t padTop = 1;
uint32_t padRight = 1;
uint32_t padBottom = 1;
return Convolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
inputNoQuantizedValues,
inputTensorInfo,
kernelNoQuantizedValues,
kernelTensorInfo,
outputExpectedNoQuantizedValues,
outputTensorInfo,
2,
2,
layout,
padLeft,
padTop,
padRight,
padBottom,
3,
3,
biasEnabled
);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T,4> CompareConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
armnn::IWorkloadFactory& refWorkloadFactory)
{
unsigned int inputHeight = 8;
unsigned int inputWidth = 16;
unsigned int inputChannels = 3;
unsigned int inputNum = 5;
unsigned int kernelHeight = 3;
unsigned int kernelWidth = 3;
unsigned int strideX = 2;
unsigned int strideY = 3;
unsigned int padX = 1;
unsigned int padY = 1;
unsigned int outputNum = inputNum;
unsigned int outputChannels = 2;
unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;
unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;
armnn::TensorInfo inputTensorInfo;
armnn::TensorInfo outputTensorInfo;
armnn::TensorInfo kernelDesc;
armnn::TensorInfo biasDesc;
unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};
unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
unsigned int kernelShape[] = {outputChannels, inputChannels, kernelHeight, kernelWidth};
unsigned int biasShape[] = {outputChannels};
inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType);
outputTensorInfo = armnn::TensorInfo(4, outputShape, ArmnnType);
kernelDesc = armnn::TensorInfo(4, kernelShape, ArmnnType);
biasDesc = armnn::TensorInfo(1, biasShape, ArmnnType);
LayerTestResult<T,4> ret(outputTensorInfo);
auto input = MakeRandomTensor<T, 4>(inputTensorInfo, 124908);
auto kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234);
auto bias = MakeRandomTensor<T, 1>(biasDesc, 1028);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::Convolution2dQueueDescriptor data;
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
data.m_Bias = &biasTensor;
data.m_Parameters.m_StrideX = strideX;
data.m_Parameters.m_StrideY = strideY;
data.m_Parameters.m_PadLeft = padX;
data.m_Parameters.m_PadRight = padX;
data.m_Parameters.m_PadTop = padY;
data.m_Parameters.m_PadBottom = padY;
data.m_Parameters.m_BiasEnabled = true;
std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
armnn::Convolution2dQueueDescriptor refData = data;
armnn::WorkloadInfo refInfo = info;
SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateConvolution2d(refData, refInfo);
outputHandleRef->Allocate();
inputHandleRef->Allocate();
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
ExecuteWorkload(*workload, memoryManager);
workloadRef->PostAllocationConfigure();
workloadRef->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
return ret;
}
//
// DepthwiseConvolution2d implementations
//
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const boost::multi_array<T, 4>& input,
const boost::multi_array<T, 4>& kernel,
const boost::multi_array<B, 1>& bias,
const boost::multi_array<T, 4>& outputExpected,
float qScale,
int32_t qOffset,
const armnn::DataLayout layout,
uint32_t padLeft = 0,
uint32_t padTop = 0,
uint32_t padRight = 0,
uint32_t padBottom = 0,
uint32_t strideX = 1,
uint32_t strideY = 1)
{
unsigned int inputNum = boost::numeric_cast<unsigned int>(input.shape()[0]);
unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[1]);
unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[2]);
unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[3]);
unsigned int kernelChanMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
unsigned int outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
// If a bias is used, its size must equal the number of output channels.
bool biasEnabled = bias.size() > 0;
BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
// Creates the tensors.
armnn::TensorInfo inputTensorInfo =
armnnUtils::GetTensorInfo(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo =
armnnUtils::GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if (armnn::IsQuantizedType<T>())
{
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
kernelDesc.SetQuantizationScale(qScale);
kernelDesc.SetQuantizationOffset(qOffset);
biasDesc.SetQuantizationScale(qScale*qScale);
biasDesc.SetQuantizationOffset(0);
}
// Construct the input data.
std::vector<T> inputData;
inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth);
// At this point if we require it permute the input data
const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(inputData.size());
armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(T));
inputData = tmp;
}
auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
// Construct the output data, with bias applied, as appropriate.
std::vector<T> outputData;
outputData.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);
if (biasEnabled)
{
std::vector<T> biasV;
biasV.assign(bias.data(), bias.data() + outputChannels);
ApplyBias(outputData, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
outputWidth, outputHeight);
}
LayerTestResult<T, 4> ret(outputTensorInfo);
// At this point if we require it permute the expected output
if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(outputData.size());
armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data(), sizeof(T));
outputData = tmp;
}
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
if (biasEnabled)
{
AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
}
armnn::DepthwiseConvolution2dQueueDescriptor data;
data.m_Weight = &weightsTensor;
data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs.
data.m_Parameters.m_StrideX = strideX;
data.m_Parameters.m_StrideY = strideY;
data.m_Parameters.m_PadLeft = padLeft;
data.m_Parameters.m_PadRight = padRight;
data.m_Parameters.m_PadTop = padTop;
data.m_Parameters.m_PadBottom = padBottom;
data.m_Parameters.m_BiasEnabled = biasEnabled;
data.m_Parameters.m_DataLayout = layout;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
ExecuteWorkload(*workload, memoryManager);
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
return ret;
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled,
const armnn::DataLayout layout)
{
using B = armnn::ResolveType<ArmnnBType>;
unsigned int inputHeight = 3;
unsigned int inputWidth = 3;
unsigned int inputChannels = 2;
unsigned int inputNum = 1;
unsigned int kernelHeight = 3;
unsigned int kernelWidth = 3;
unsigned int kernelChannels = inputChannels;
unsigned int kernelDepthMultiplier = 1;
unsigned int outputHeight = 1;
unsigned int outputWidth = 1;
unsigned int outputChannels = kernelChannels;
unsigned int outputNum = inputNum;
armnn::TensorInfo inputTensorInfo =
armnnUtils::GetTensorInfo(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo =
armnnUtils::GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
armnn::TensorInfo kernelDesc({kernelDepthMultiplier, kernelChannels, kernelHeight, kernelWidth},
ArmnnType);
armnn::TensorInfo biasDesc({ outputChannels }, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
{
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
kernelDesc.SetQuantizationScale(qScale);
kernelDesc.SetQuantizationOffset(qOffset);
biasDesc.SetQuantizationScale(qScale*qScale);
biasDesc.SetQuantizationOffset(0);
}
std::vector<T> inputData = std::vector<T>(
QuantizedVector<T>({
1.f, 2.f, 1.f,
2.f, 1.f, 2.f,
1.f, 2.f, 1.f,
1.f, 2.f, 1.f,
2.f, 1.f, 2.f,
1.f, 2.f, 1.f,
},
inputTensorInfo.GetQuantizationScale(),
inputTensorInfo.GetQuantizationOffset()));
// at this point if we require it permute the input data
const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(inputData.size());
armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(T));
inputData = tmp;
}
auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
std::vector<B> biasV(QuantizedVector<B>({ 0, 2 },
biasDesc.GetQuantizationScale(),
biasDesc.GetQuantizationOffset()));
auto bias = MakeTensor<B, 1>(biasDesc, biasV);
std::vector<T> kernelData = std::vector<T>(
QuantizedVector<T>({
1.f, 0.f, 1.f,
0.f, 0.f, 0.f,
-1.f, 0.f, -1.f,
1.f, 0.f, 1.f,
0.f, 0.f, 0.f,
-1.f, 0.f, -1.f,
},
kernelDesc.GetQuantizationScale(),
kernelDesc.GetQuantizationOffset()));
auto kernel = MakeTensor<T, 4>(kernelDesc, kernelData);
// Manually calculated.
std::vector<T> outputImage(
QuantizedVector<T>({ 0.f, 0.f },
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset())
);
// Optionally apply bias to output image.
if(biasEnabled)
{
ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
outputWidth, outputHeight);
}
LayerTestResult<T, 4> ret(outputTensorInfo);
if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(outputImage.size());
armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputImage.data(), tmp.data(), sizeof(T));
outputImage = tmp;
}
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::DepthwiseConvolution2dQueueDescriptor data;
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
data.m_Parameters.m_StrideX = 1;
data.m_Parameters.m_StrideY = 1;
data.m_Parameters.m_PadLeft = 0;
data.m_Parameters.m_PadRight = 0;
data.m_Parameters.m_PadTop = 0;
data.m_Parameters.m_PadBottom = 0;
data.m_Parameters.m_BiasEnabled = biasEnabled;
data.m_Parameters.m_DataLayout = layout;
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
ExecuteWorkload(*workload, memoryManager);
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
return ret;
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled,
const armnn::DataLayout layout)
{
using B = armnn::ResolveType<ArmnnBType>;
unsigned int depthMultiplier = 2;
unsigned int inputHeight = 8;
unsigned int inputWidth = 16;
unsigned int inputChannels = 2;
unsigned int inputBatchSize = 1;
unsigned int kernelHeight = 5;
unsigned int kernelWidth = 3;
unsigned int outputHeight = inputHeight - kernelHeight + 1 + 2;
unsigned int outputWidth = (inputWidth - kernelWidth + 1)/2;
unsigned int outputChannels = inputChannels * depthMultiplier;
unsigned int outputBatchSize = inputBatchSize;
armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo(
inputBatchSize, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo(
outputBatchSize, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
armnn::TensorInfo kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth},
ArmnnType);
armnn::TensorInfo biasDesc({outputChannels}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
{
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
kernelDesc.SetQuantizationScale(qScale);
kernelDesc.SetQuantizationOffset(qOffset);
biasDesc.SetQuantizationScale(qScale*qScale);
biasDesc.SetQuantizationOffset(0);
}
// NOTE: originalInputData is in NCHW format
std::vector<T> originalInputData = std::vector<T>(
QuantizedVector<T>({
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f
},
inputTensorInfo.GetQuantizationScale(),
inputTensorInfo.GetQuantizationOffset()));
std::vector<T> inputData = originalInputData;
// at this point if we require it permute the input data
const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
if (layout == armnn::DataLayout::NHWC)
{
armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC,
originalInputData.data(), inputData.data(), sizeof(T));
}
auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
std::vector<B> biasV = QuantizedVector<B>({ 0, 2, 1, -1 },
biasDesc.GetQuantizationScale(),
biasDesc.GetQuantizationOffset());
auto bias = MakeTensor<B, 1>(biasDesc, biasV);
std::vector<T> kernelData = std::vector<T>(
QuantizedVector<T>({
1, 1, 1,
1, -1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
2, 2, 2,
2, 2, 2,
2, 2, 2,
2, 2, 2,
2, 2, 2,
0, 0, 0,
0, -1, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 0, 0,
0, 1, 0,
0, 0, 0,
0, 0, 0
},
kernelDesc.GetQuantizationScale(),
kernelDesc.GetQuantizationOffset()));
auto kernel = MakeTensor<T, 4>(kernelDesc, kernelData);
// Manually calculated.
std::vector<T> originalOutputImage = std::vector<T>(
QuantizedVector<T>({
3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f,
6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f,
5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f,
6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f,
6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f,
5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f,
-0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
-0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f
},
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset()));
// Optionally apply bias to output image.
if(biasEnabled)
{
ApplyBias(originalOutputImage,
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset(),
biasV,
biasDesc.GetQuantizationScale(),
biasDesc.GetQuantizationOffset(),
outputWidth,
outputHeight);
}
LayerTestResult<T, 4> ret(outputTensorInfo);
std::vector<T> outputImage = originalOutputImage;
if (layout == armnn::DataLayout::NHWC)
{
armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC,
originalOutputImage.data(), outputImage.data(), sizeof(T));
}
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::DepthwiseConvolution2dQueueDescriptor data;
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
data.m_Parameters.m_StrideX = 2;
data.m_Parameters.m_StrideY = 1;
data.m_Parameters.m_PadLeft = 0;
data.m_Parameters.m_PadRight = 0;
data.m_Parameters.m_PadTop = 1;
data.m_Parameters.m_PadBottom = 1;
data.m_Parameters.m_BiasEnabled = biasEnabled;
data.m_Parameters.m_DataLayout = layout;
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
ExecuteWorkload(*workload, memoryManager);
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
return ret;
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const boost::multi_array<T, 4>& originalInput,
const boost::multi_array<T, 4>& originalKernel,
const boost::multi_array<B, 1>& bias,
const boost::multi_array<T, 4>& originalOutputExpected,
float qScale,
int32_t qOffset,
const armnn::DataLayout layout = armnn::DataLayout::NCHW,
uint32_t padLeft = 0,
uint32_t padTop = 0,
uint32_t padRight = 0,
uint32_t padBottom = 0,
uint32_t strideX = 1,
uint32_t strideY = 1,
uint32_t dilationX = 1,
uint32_t dilationY = 1)
{
unsigned int inputHeight = boost::numeric_cast<unsigned int>(originalInput.shape()[2]);
unsigned int inputWidth = boost::numeric_cast<unsigned int>(originalInput.shape()[3]);
unsigned int inputChannels = boost::numeric_cast<unsigned int>(originalInput.shape()[1]);
unsigned int inputNum = boost::numeric_cast<unsigned int>(originalInput.shape()[0]);
unsigned int outputHeight = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[2]);
unsigned int outputWidth = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[3]);
unsigned int outputChannels = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[1]);
unsigned int outputNum = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[0]);
unsigned int kernelHeight = boost::numeric_cast<unsigned int>(originalKernel.shape()[2]);
unsigned int kernelWidth = boost::numeric_cast<unsigned int>(originalKernel.shape()[3]);
unsigned int kernelChannels = boost::numeric_cast<unsigned int>(originalKernel.shape()[1]);
unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(originalKernel.shape()[0]);
bool biasEnabled = bias.size() > 0;
// This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).
BOOST_ASSERT(inputNum == 1);
BOOST_ASSERT(outputNum == 1);
// If a bias is used, its size must equal the number of output channels.
BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
// Note these tensors will use two (identical) batches.
armnn::TensorInfo inputTensorInfo =
armnnUtils::GetTensorInfo(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo =
armnnUtils::GetTensorInfo(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
// Kernel must be NCHW layout always, independently of the layout of the input and output for depthwise convolution.
armnn::TensorInfo kernelDesc({kernelDepthMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
{
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
kernelDesc.SetQuantizationScale(qScale);
kernelDesc.SetQuantizationOffset(qOffset);
biasDesc.SetQuantizationScale(qScale*qScale);
biasDesc.SetQuantizationOffset(0);
}
LayerTestResult<T, 4> ret(outputTensorInfo);
// Construct input data
std::vector<T> input;
input.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);
std::vector<T> inputData;
inputData.insert(inputData.end(), input.begin(), input.end());
inputData.insert(inputData.end(), input.begin(), input.end());
// at this point if we require it permute the input data
const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(inputData.size());
armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(T));
inputData = tmp;
}
auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
std::vector<T> output;
output.assign(originalOutputExpected.data(),
originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);
// Apply bias to output data if it is enabled.
if(biasEnabled)
{
std::vector<T> biasV;
biasV.assign(bias.data(), bias.data() + outputChannels);
ApplyBias(output, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
outputWidth, outputHeight);
}
// Construct expected output data
std::vector<T> outputData;
outputData.insert(outputData.end(), output.begin(), output.end());
outputData.insert(outputData.end(), output.begin(), output.end());
// at this point if we require it permute the expected output
if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(outputData.size());
armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data(), sizeof(T));
outputData = tmp;
}
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::DepthwiseConvolution2dQueueDescriptor data;
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
if(biasEnabled)
{
AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
}
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
data.m_Parameters.m_StrideX = strideX;
data.m_Parameters.m_StrideY = strideY;
data.m_Parameters.m_PadLeft = padLeft;
data.m_Parameters.m_PadRight = padRight;
data.m_Parameters.m_PadTop = padTop;
data.m_Parameters.m_PadBottom = padBottom;
data.m_Parameters.m_BiasEnabled = biasEnabled;
data.m_Parameters.m_DataLayout = layout;
data.m_Parameters.m_DilationX = dilationX;
data.m_Parameters.m_DilationY = dilationY;
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
ExecuteWorkload(*workload, memoryManager);
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
return ret;
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled,
const armnn::DataLayout layout)
{
// Use a single-batch 2-channel 5x5 image as input.
armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, ArmnnType);
auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
QuantizedVector<T>({
0, 1, 2, 3, 4,
5, 6, 7, 8, 9,
10, 11, 12, 13, 14,
15, 16, 17, 18, 19,
20, 21, 22, 23, 24,
25, 26, 27, 28, 29,
30, 31, 32, 33, 34,
35, 36, 37, 38, 39,
40, 41, 42, 43, 44,
45, 46, 47, 48, 49
},
inputTensorInfo.GetQuantizationScale(),
inputTensorInfo.GetQuantizationOffset())));
// Use a depth multiplier of 1 on a 2-channel 4x4 kernel.
armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType);
auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(
QuantizedVector<T>({
32, 31, 30, 29,
28, 27, 26, 25,
24, 23, 22, 21,
20, 19, 18, 17,
16, 15, 14, 13,
12, 11, 10, 9,
8, 7, 6, 5,
4, 3, 2, 1
},
kernelTensorInfo.GetQuantizationScale(),
kernelTensorInfo.GetQuantizationOffset())));
// Expected output is 1 batch of a 2-channel 5x5 image.
// Calculated using the python tensorflow library with strideX=1, strideY=1.
armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, ArmnnType);
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(
QuantizedVector<T>({
1062, 1580, 1850, 1530, 1117,
2140, 3108, 3500, 2842, 2042,
3580, 5068, 5460, 4342, 3062,
3618, 5072, 5390, 4248, 2971,
3074, 4282, 4510, 3533, 2457,
1550, 2284, 2362, 1955, 1428,
2910, 4206, 4342, 3528, 2536,
3390, 4886, 5022, 4068, 2916,
3566, 5056, 5182, 4133, 2922,
3100, 4352, 4452, 3517, 2465
},
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset())));
return DepthwiseConvolution2dAsymmetricTestImpl<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
input,
kernel,
GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
expectedOutput,
qScale,
qOffset,
layout,
1, // Padding left.
1, // Padding top.
2, // Padding right.
2, // Padding bottom.
1, // strideX
1); // strideY
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled)
{
auto layout = armnn::DataLayout::NHWC;
armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5}, ArmnnType);
auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
QuantizedVector<T>({
0, 1, 2, 3, 4,
5, 6, 7, 8, 9,
10, 11, 12, 13, 14,
15, 16, 17, 18, 19,
20, 21, 22, 23, 24,
25, 26, 27, 28, 29,
30, 31, 32, 33, 34,
35, 36, 37, 38, 39,
40, 41, 42, 43, 44,
45, 46, 47, 48, 49
},
inputTensorInfo.GetQuantizationScale(),
inputTensorInfo.GetQuantizationOffset())));
armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType);
auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(
QuantizedVector<T>({
32, 31, 30, 29,
28, 27, 26, 25,
24, 23, 22, 21,
20, 19, 18, 17,
16, 15, 14, 13,
12, 11, 10, 9,
8, 7, 6, 5,
4, 3, 2, 1
},
kernelTensorInfo.GetQuantizationScale(),
kernelTensorInfo.GetQuantizationOffset())));
armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5}, ArmnnType);
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(
QuantizedVector<T>({
1062, 1580, 1850, 1530, 1117,
2140, 3108, 3500, 2842, 2042,
3580, 5068, 5460, 4342, 3062,
3618, 5072, 5390, 4248, 2971,
3074, 4282, 4510, 3533, 2457,
1550, 2284, 2362, 1955, 1428,
2910, 4206, 4342, 3528, 2536,
3390, 4886, 5022, 4068, 2916,
3566, 5056, 5182, 4133, 2922,
3100, 4352, 4452, 3517, 2465
},
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset())));
return DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
input,
kernel,
GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
expectedOutput,
qScale,
qOffset,
layout,
1, // Padding left.
1, // Padding top.
2, // Padding right.
2, // Padding bottom.
1, // strideX
1); // strideY
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float qScale,
int32_t qOffset,
bool biasEnabled)
{
auto layout = armnn::DataLayout::NHWC;
armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9}, ArmnnType);
auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
QuantizedVector<T>({
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
},
inputTensorInfo.GetQuantizationScale(),
inputTensorInfo.GetQuantizationOffset())));
armnn::TensorInfo kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);
auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(
QuantizedVector<T>({
1, 2, 3,
4, 5, 6,
7, 8, 9
},
kernelTensorInfo.GetQuantizationScale(),
kernelTensorInfo.GetQuantizationOffset())));
uint32_t padLeft = 0;
uint32_t padTop = 0;
uint32_t padRight = 0;
uint32_t padBottom = 0;
uint32_t strideX = 1;
uint32_t strideY = 1;
uint32_t dilationX = 3;
uint32_t dilationY = 3;
// Since the dilation rate is 3 this will reduce the size of the output from 9x9 to 3x3 of all 5s.
armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3}, ArmnnType);
boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(
QuantizedVector<T>({
5, 5, 5,
5, 5, 5,
5, 5, 5
},
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset())));
return DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
input,
kernel,
GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
expectedOutput,
qScale,
qOffset,
layout,
padLeft,
padTop,
padRight,
padBottom,
strideX,
strideY,
dilationX,
dilationY);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> DepthwiseConvolution2d3x3DilationTestCommon(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const std::vector<float>& inputNoQuantizedValues,
armnn::TensorInfo& inputTensorInfo,
const std::vector<float>& kernelNoQuantizedValues,
armnn::TensorInfo& kernelTensorInfo,
const std::vector<float>& outputExpectedNoQuantizedValues,
armnn::TensorInfo& outputTensorInfo,
uint32_t dilationX,
uint32_t dilationY,
armnn::DataLayout layout = armnn::DataLayout::NCHW,
bool biasEnabled = false)
{
float qScale;
int32_t qOffset;
switch (ArmnnType)
{
case armnn::DataType::QAsymmU8:
{
qScale = 0.1f;
qOffset = 128;
break;
}
case armnn::DataType::QSymmS16:
{
qScale = 0.1f;
qOffset = 0;
break;
}
case armnn::DataType::Float32:
default:
{
qScale = 0.f;
qOffset = 0;
break;
}
}
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
kernelTensorInfo.SetQuantizationScale(qScale);
kernelTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
auto input = MakeTensor<T, 4>(inputTensorInfo,
std::vector<T>(QuantizedVector<T>(inputNoQuantizedValues,
inputTensorInfo.GetQuantizationScale(),
inputTensorInfo.GetQuantizationOffset())));
auto kernel = MakeTensor<T, 4>(kernelTensorInfo,
std::vector<T>(QuantizedVector<T>(kernelNoQuantizedValues,
kernelTensorInfo.GetQuantizationScale(),
kernelTensorInfo.GetQuantizationOffset())));
auto expectedOutput =
MakeTensor<T, 4>(outputTensorInfo,
std::vector<T>(QuantizedVector<T>(outputExpectedNoQuantizedValues,
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset())));
uint32_t padLeft = 0;
uint32_t padTop = 0;
uint32_t padRight = 0;
uint32_t padBottom = 0;
uint32_t strideX = 1;
uint32_t strideY = 1;
return DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
input,
kernel,
GetBias<ArmnnBType>(biasEnabled, qScale * qScale, outputTensorInfo, layout),
expectedOutput,
qScale,
qOffset,
layout,
padLeft,
padTop,
padRight,
padBottom,
strideX,
strideY,
dilationX,
dilationY);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
LayerTestResult<T, 4> DepthwiseConvolution2d3x3Dilation3x3Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
armnn::TensorInfo inputTensorInfo({1, 1, 10, 10}, ArmnnType);
std::vector<float> inputNoQuantizedValues =
{
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0
};
armnn::TensorInfo kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);
std::vector<float> kernelNoQuantizedValues =
{
1, 2, 3,
4, 5, 6,
7, 8, 9
};
// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,
// therefore the output will be 4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1
armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);
std::vector<float> outputExpectedNoQuantizedValues =
{
6., 5., 5., 5.,
6., 5., 5., 5.,
6., 5., 5., 5.,
3., 2., 2., 2.
};
return DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
inputNoQuantizedValues,
inputTensorInfo,
kernelNoQuantizedValues,
kernelTensorInfo,
outputExpectedNoQuantizedValues,
outputTensorInfo,
3,
3,
layout,
biasEnabled);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
LayerTestResult<T, 4> DepthwiseConvolution2d2x3x3Dilation3x3Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
armnn::TensorInfo inputTensorInfo({1, 2, 10, 10}, ArmnnType);
std::vector<float> inputNoQuantizedValues =
{
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0
};
armnn::TensorInfo kernelTensorInfo({ 1, 2, 3, 3}, ArmnnType);
std::vector<float> kernelNoQuantizedValues =
{
1, 2, 3,
4, 5, 6,
7, 8, 9,
1, 2, 3,
4, 5, 6,
7, 8, 9
};
// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,
// therefore the output will be 2x4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1
armnn::TensorInfo outputTensorInfo({ 1, 2, 4, 4}, ArmnnType);
std::vector<float> outputExpectedNoQuantizedValues =
{
6., 5., 5., 5.,
6., 5., 5., 5.,
6., 5., 5., 5.,
3., 2., 2., 2.,
6., 5., 5., 5.,
6., 5., 5., 5.,
6., 5., 5., 5.,
3., 2., 2., 2.
};
return DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
inputNoQuantizedValues,
inputTensorInfo,
kernelNoQuantizedValues,
kernelTensorInfo,
outputExpectedNoQuantizedValues,
outputTensorInfo,
3,
3,
layout,
biasEnabled);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
LayerTestResult<T, 4> DepthwiseConvolution2dMult4Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
armnn::TensorInfo inputTensorInfo({1, 2, 3, 3}, ArmnnType);
std::vector<float> inputNoQuantizedValues =
{
10.0, 10.0, 10.0,
10.0, 10.0, 10.0,
10.0, 10.0, 10.0,
21.0, 22.0, 23.0,
24.0, 25.0, 26.0,
27.0, 28.0, 29.0
};
armnn::TensorInfo kernelTensorInfo({ 4, 2, 2, 2}, ArmnnType);
std::vector<float> kernelNoQuantizedValues =
{
0.25f, 0.25f,
0.25f, 0.25f,
0.25f, 0.25f,
0.25f, 0.25f,
0.0f , 0.0f,
0.0f , 0.1f,
0.0f , 0.0f,
0.0f , 0.1f,
0.2f , 0.0f,
0.0f , 0.0f,
0.2f , 0.0f,
0.0f , 0.0f,
0.0f , 0.3f,
0.0f , 0.0f,
0.0f , 0.3f,
0.0f , 0.0f
};
armnn::TensorInfo outputTensorInfo({ 1, 8, 2, 2}, ArmnnType);
std::vector<float> outputExpectedNoQuantizedValues =
{
10.f, 10.f,
10.f, 10.f,
1.f, 1.f,
1.f, 1.f,
2.f, 2.f,
2.f, 2.f,
3.f, 3.f,
3.f, 3.f,
23.f, 24.f,
26.f, 27.f,
2.5f, 2.6000001f,
2.8f, 2.9f,
4.2000003f, 4.4f,
4.8f, 5.f,
6.6000004f, 6.9f,
7.5000005f, 7.8f
};
return DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
inputNoQuantizedValues,
inputTensorInfo,
kernelNoQuantizedValues,
kernelTensorInfo,
outputExpectedNoQuantizedValues,
outputTensorInfo,
1,
1,
layout,
biasEnabled);
}
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
LayerTestResult<T, 4> DepthwiseConvolution2dMult2Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
armnn::TensorInfo inputTensorInfo({1, 2, 3, 3}, ArmnnType);
std::vector<float> inputNoQuantizedValues =
{
10.0, 10.0, 10.0,
10.0, 10.0, 10.0,
10.0, 10.0, 10.0,
21.0, 22.0, 23.0,
24.0, 25.0, 26.0,
27.0, 28.0, 29.0
};
armnn::TensorInfo kernelTensorInfo({ 2, 2, 2, 2}, ArmnnType);
std::vector<float> kernelNoQuantizedValues =
{
0.25f, 0.25f,
0.25f, 0.25f,
0.2f , 0.0f,
0.0f , 0.0f,
0.0f , 0.0f,
0.0f , 0.1f,
0.0f , 0.3f,
0.0f , 0.0f
};
armnn::TensorInfo outputTensorInfo({ 1, 4, 2, 2}, ArmnnType);
std::vector<float> outputExpectedNoQuantizedValues =
{
10.f, 10.f,
10.f, 10.f,
1.f, 1.f,
1.f, 1.f,
4.2000003f, 4.4f,
4.8f, 5.f,
6.6000004f, 6.9f,
7.5000005f, 7.8f
};
return DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
workloadFactory,
memoryManager,
inputNoQuantizedValues,
inputTensorInfo,
kernelNoQuantizedValues,
kernelTensorInfo,
outputExpectedNoQuantizedValues,
outputTensorInfo,
1,
1,
layout,
biasEnabled);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
armnn::IWorkloadFactory& refWorkloadFactory,
const armnnUtils::DataLayoutIndexed& layout)
{
unsigned int inputHeight = 8;
unsigned int inputWidth = 16;
unsigned int inputChannels = 3;
unsigned int inputNum = 5;
unsigned int kernelHeight = 3;
unsigned int kernelWidth = 3;
unsigned int channelMultiplier = 1;
unsigned int strideX = 2;
unsigned int strideY = 3;
unsigned int padX = 1;
unsigned int padY = 1;
unsigned int outputNum = inputNum;
unsigned int outputChannels = inputChannels * channelMultiplier;
unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;
unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;
armnn::TensorInfo inputTensorInfo;
armnn::TensorInfo outputTensorInfo;
armnn::TensorInfo kernelDesc;
armnn::TensorInfo biasDesc;
std::vector<unsigned int> inputShape;
std::vector<unsigned int> outputShape;
std::vector<unsigned int> kernelShape{ channelMultiplier, inputChannels, kernelHeight, kernelWidth };
std::vector<unsigned int> biasShape{ outputChannels };
switch (layout.GetDataLayout())
{
case armnn::DataLayout::NCHW:
inputShape = { inputNum, inputChannels, inputHeight, inputWidth };
outputShape = { outputNum, outputChannels, outputHeight, outputWidth };
break;
case armnn::DataLayout ::NHWC:
inputShape = { inputNum, inputHeight, inputWidth, inputChannels };
outputShape = { outputNum, outputHeight, outputWidth, outputChannels };
break;
default:
throw armnn::InvalidArgumentException("unknown data layout ["
+ std::to_string(static_cast<int>(layout.GetDataLayout())) + "]");
}
float inputsQScale = armnn::IsQuantizedType<T>() ? 1.0f : 0;
float outputQScale = armnn::IsQuantizedType<T>() ? 2.0f : 0;
int32_t qOffset = 0;
inputTensorInfo = armnn::TensorInfo(4, inputShape.data(), ArmnnType, inputsQScale, qOffset);
outputTensorInfo = armnn::TensorInfo(4, outputShape.data(), ArmnnType, outputQScale, qOffset);
kernelDesc = armnn::TensorInfo(4, kernelShape.data(), ArmnnType, inputsQScale, qOffset);
biasDesc = armnn::TensorInfo(
1, biasShape.data(), armnn::GetBiasDataType(ArmnnType), inputsQScale, qOffset);
LayerTestResult<T, 4> ret(outputTensorInfo);
auto input = MakeRandomTensor<T, 4>(inputTensorInfo, 124908, 0.0f, 255.0f);
auto kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234, 0.0f, 255.0f);
auto bias = MakeRandomTensor<typename FullyConnectedBiasTypeForInputType<T>::Type, 1>(
biasDesc, 1028, 0.0f, 255.0f);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::DepthwiseConvolution2dQueueDescriptor data;
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
data.m_Bias = &biasTensor;
data.m_Parameters.m_StrideX = strideX;
data.m_Parameters.m_StrideY = strideY;
data.m_Parameters.m_PadLeft = padX;
data.m_Parameters.m_PadRight = padX;
data.m_Parameters.m_PadTop = padY;
data.m_Parameters.m_PadBottom = padY;
data.m_Parameters.m_BiasEnabled = true;
data.m_Parameters.m_DataLayout = layout.GetDataLayout();
std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
armnn::DepthwiseConvolution2dQueueDescriptor refData = data;
armnn::WorkloadInfo refInfo = info;
SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateDepthwiseConvolution2d(refData, refInfo);
outputHandleRef->Allocate();
inputHandleRef->Allocate();
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
ExecuteWorkload(*workload, memoryManager);
workloadRef->PostAllocationConfigure();
workloadRef->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
return ret;
}
//
// Explicit template specializations
//
template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
Convolution2d3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8>, 4>
Convolution2d3x3Dilation3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16>, 4>
Convolution2d3x3Dilation3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
Convolution2d2x3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8>, 4>
Convolution2d2x3x3Dilation3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16>, 4>
Convolution2d2x3x3Dilation3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
armnn::IWorkloadFactory &workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
bool biasEnabled,
const armnn::DataLayout layout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8>, 4>
Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
armnn::IWorkloadFactory &workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
bool biasEnabled,
const armnn::DataLayout layout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16>, 4>
Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(
armnn::IWorkloadFactory &workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
bool biasEnabled,
const armnn::DataLayout layout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8>, 4>
DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16>, 4>
DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8>, 4>
DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16>, 4>
DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(
armnn::IWorkloadFactory&,
const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
bool,
armnn::DataLayout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
DepthwiseConvolution2dMult4Test<armnn::DataType::Float32, armnn::DataType::Float32>(
armnn::IWorkloadFactory &workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
bool biasEnabled,
const armnn::DataLayout layout);
template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
DepthwiseConvolution2dMult2Test<armnn::DataType::Float32, armnn::DataType::Float32>(
armnn::IWorkloadFactory &workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
bool biasEnabled,
const armnn::DataLayout layout);
//
// Implementation functions
//
LayerTestResult<float, 4> SimpleConvolution2d3x5Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return SimpleConvolution2d3x5TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout);
}
LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return SimpleConvolution2d3x5TestCommon<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
}
LayerTestResult<float, 4> SimpleConvolution2d3x3Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return SimpleConvolution2d3x3TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout);
}
LayerTestResult<float, 4> SimpleConvolution2d3x3NhwcTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled)
{
return SimpleConvolution2d3x3NhwcTestCommon<armnn::DataType::Float32>(
workloadFactory,
memoryManager,
0.f,
0,
biasEnabled,
armnn::DataLayout::NHWC);
}
LayerTestResult<float, 4> SimpleConvolution2d3x3Stride2x2Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return SimpleConvolution2d3x3Stride2x2TestCommon<armnn::DataType::Float32>(
workloadFactory,
memoryManager,
0.f,
0,
biasEnabled,
layout);
}
LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return SimpleConvolution2d3x3TestCommon<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
}
LayerTestResult<int16_t, 4> SimpleConvolution2d3x5QSymm16Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return SimpleConvolution2d3x5TestCommon<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(
workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
}
LayerTestResult<int16_t, 4> SimpleConvolution2d3x3QSymm16Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return SimpleConvolution2d3x3TestCommon<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(
workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
}
LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
armnn::DataLayout layout)
{
return SimpleConvolution2dAsymmetricPaddingTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory, memoryManager, layout, 0.0f, 0);
}
LayerTestResult<float, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
armnn::DataLayout layout)
{
return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon
<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory, memoryManager, layout, 0.0f, 0);
}
LayerTestResult<float, 4> Convolution1dTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled)
{
return Convolution1dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory, memoryManager, 0.0f, 0, biasEnabled);
}
LayerTestResult<uint8_t, 4> Convolution1dUint8Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled)
{
return Convolution1dTestImpl<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
workloadFactory, memoryManager, 0.1f, 128, biasEnabled);
}
LayerTestResult<uint8_t, 4> Convolution2dPerAxisQuantTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::DataLayout layout)
{
using namespace armnn;
const DataType inputType = DataType::QAsymmU8;
const DataType kernelType = DataType::QuantizedSymm8PerAxis;
const DataType biasType = DataType::Signed32;
TensorInfo inputInfo ({ 1, 3, 1, 2 }, inputType, 0.5f, 128);
TensorInfo outputInfo({ 1, 3, 1, 3 }, inputType, 1.0f, 128);
const std::vector<float> quantScales{ 0.5f, 0.75f, 1.0f };
constexpr unsigned int quantDimension = 0;
TensorInfo kernelInfo({ 3, 1, 1, 2 }, kernelType, quantScales, quantDimension);
const std::vector<float> biasQuantScales{ 0.25f, 0.375f, 0.5f };
TensorInfo biasInfo({ 3 }, biasType, biasQuantScales, quantDimension);
std::vector<uint8_t> inputData =
{
138, 108, 138, 108, 138, 108
};
std::vector<int8_t> kernelData =
{
1, 2, 1, 2, 1, 2
};
std::vector<int32_t> biasData =
{
4, 4, 4
};
std::vector<uint8_t> expectedOutputData =
{
121, 118, 115, 121, 118, 115, 121, 118, 115
};
if (layout == DataLayout::NCHW)
{
PermuteTensorNhwcToNchw(inputInfo, inputData);
PermuteTensorNhwcToNchw(kernelInfo, kernelData);
PermuteTensorNhwcToNchw(outputInfo, expectedOutputData);
}
Convolution2dDescriptor descriptor;
descriptor.m_StrideX = 1;
descriptor.m_StrideY = 1;
descriptor.m_PadLeft = 0;
descriptor.m_PadRight = 0;
descriptor.m_PadTop = 0;
descriptor.m_PadBottom = 0;
descriptor.m_BiasEnabled = true;
descriptor.m_DataLayout = layout;
std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputInfo);
std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo);
WorkloadInfo workloadInfo;
ScopedCpuTensorHandle weightTensor(kernelInfo);
ScopedCpuTensorHandle biasTensor(biasInfo);
AllocateAndCopyDataToITensorHandle(&weightTensor, kernelData.data());
AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
Convolution2dQueueDescriptor queueDescriptor;
queueDescriptor.m_Parameters = descriptor;
queueDescriptor.m_Weight = &weightTensor;
queueDescriptor.m_Bias = &biasTensor;
AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, inputHandle.get());
AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, outputHandle.get());
std::unique_ptr<IWorkload> workload = workloadFactory.CreateConvolution2d(queueDescriptor, workloadInfo);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputData.data());
ExecuteWorkload(*workload, memoryManager);
LayerTestResult<uint8_t, 4> ret(outputInfo);
CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get());
ret.outputExpected = MakeTensor<uint8_t, 4>(outputInfo, expectedOutputData);
return ret;
}
LayerTestResult<float,4> CompareConvolution2dTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
armnn::IWorkloadFactory& refWorkloadFactory)
{
return CompareConvolution2dTestImpl<armnn::DataType::Float32>(
workloadFactory, memoryManager, refWorkloadFactory);
}
LayerTestResult<float, 4> DepthwiseConvolution2dTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return DepthwiseConvolution2dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);
}
LayerTestResult<float, 4> DepthwiseConvolution2dDepthNhwcTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled)
{
return DepthwiseConvolution2dNhwcTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory, memoryManager, 0.0f, 0, biasEnabled);
}
LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);
}
LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul64Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
{
armnn::TensorInfo inputTensorInfo({ 1, 1, 2, 2 }, armnn::DataType::Float32);
auto input = MakeTensor<float, 4>(inputTensorInfo, { 1.f, 2.f, 3.f, 4.f });
std::vector<float> kernelData;
std::vector<float> singleDepthKernel{ 1.f, -1.f, -1.f, 1.f };
for (unsigned int i = 0; i < 64; ++i)
{
kernelData.insert(kernelData.end(), singleDepthKernel.begin(), singleDepthKernel.end());
}
armnn::TensorInfo kernelTensorInfo({ 64, 1, 2, 2 }, armnn::DataType::Float32);
auto kernel = MakeTensor<float, 4>(kernelTensorInfo, kernelData);
std::vector<float> expectedOutputData(64, 0.f);
armnn::TensorInfo outputTensorInfo({ 1, 64, 1, 1 }, armnn::DataType::Float32);
auto expectedOutput = MakeTensor<float, 4>(outputTensorInfo, expectedOutputData);
return DepthwiseConvolution2dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory,
memoryManager,
input,
kernel,
boost::multi_array<float, 1>(),
expectedOutput,
0.f,
0,
armnn::DataLayout::NCHW);
}
LayerTestResult<float, 4> DepthwiseConvolution2dAsymmetricTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return DepthwiseConvolution2dAsymmetricTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);
}
LayerTestResult<uint8_t, 4> DepthwiseConvolution2dUint8Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return DepthwiseConvolution2dTestImpl<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
}
LayerTestResult<uint8_t, 4> DepthwiseConvolution2dDepthMul1Uint8Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(
workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
}
LayerTestResult<float, 4> SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
{
return SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
workloadFactory,
memoryManager,
0.f,
0,
false);
}
LayerTestResult<int16_t, 4> DepthwiseConvolution2dInt16Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return DepthwiseConvolution2dTestImpl<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(
workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
}
LayerTestResult<int16_t, 4> DepthwiseConvolution2dDepthMul1Int16Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
bool biasEnabled,
const armnn::DataLayout layout)
{
return DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(
workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
}
LayerTestResult<uint8_t, 4> DepthwiseConvolution2dPerAxisQuantTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::DataLayout layout)
{
using namespace armnn;
const DataType inputType = DataType::QAsymmU8;
const DataType kernelType = DataType::QuantizedSymm8PerAxis;
const DataType biasType = DataType::Signed32;
TensorInfo inputInfo ({ 1, 3, 3, 2 }, inputType, 0.5f, 128); // N H W C
TensorInfo outputInfo({ 1, 2, 2, 4 }, inputType, 1.0f, 128); // N H W C
const std::vector<float> quantScales{ 1.0f, 0.5f, 1.0f, 0.5f };
const unsigned int quantDimension = 0;
TensorInfo kernelInfo({ 2, 2, 2, 2 }, kernelType, quantScales, quantDimension); // M I H W
const std::vector<float> biasQuantScales{ 0.5f, 0.25f, 0.5f, 0.25f };
constexpr unsigned int biasQuantDimension = 0;
TensorInfo biasInfo({ 4 }, biasType, biasQuantScales, biasQuantDimension);
std::vector<uint8_t> inputData =
{
129, 130,
129, 130,
129, 130,
129, 130,
129, 130,
129, 130,
129, 130,
129, 130,
129, 130
};
std::vector<int8_t> kernelData =
{
1, 1, 1, 1,
1, 1, 1, 1,
1, 1, 1, 1,
1, 1, 1, 1
};
std::vector<int32_t> biasData =
{
4, 4, 4, 4
};
std::vector<uint8_t> expectedOutputData =
{
132, 130, 134, 131,
132, 130, 134, 131,
132, 130, 134, 131,
132, 130, 134, 131
};
if (layout == DataLayout::NCHW)
{
PermuteTensorNhwcToNchw(inputInfo, inputData);
PermuteTensorNhwcToNchw(outputInfo, expectedOutputData);
}
DepthwiseConvolution2dDescriptor descriptor;
descriptor.m_StrideX = 1;
descriptor.m_StrideY = 1;
descriptor.m_PadLeft = 0;
descriptor.m_PadRight = 0;
descriptor.m_PadTop = 0;
descriptor.m_PadBottom = 0;
descriptor.m_DilationX = 1;
descriptor.m_DilationY = 1;
descriptor.m_BiasEnabled = true;
descriptor.m_DataLayout = layout;
std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputInfo);
std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo);
WorkloadInfo workloadInfo;
ScopedCpuTensorHandle weightTensor(kernelInfo);
ScopedCpuTensorHandle biasTensor(biasInfo);
AllocateAndCopyDataToITensorHandle(&weightTensor, kernelData.data());
AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
DepthwiseConvolution2dQueueDescriptor queueDescriptor;
queueDescriptor.m_Parameters = descriptor;
queueDescriptor.m_Weight = &weightTensor;
queueDescriptor.m_Bias = &biasTensor;
AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, inputHandle.get());
AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, outputHandle.get());
std::unique_ptr<IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(queueDescriptor, workloadInfo);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputData.data());
ExecuteWorkload(*workload, memoryManager);
LayerTestResult<uint8_t, 4> ret(outputInfo);
CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get());
ret.outputExpected = MakeTensor<uint8_t, 4>(outputInfo, expectedOutputData);
return ret;
}
LayerTestResult<float, 4> CompareDepthwiseConvolution2dFloatTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
armnn::IWorkloadFactory& refWorkloadFactory,
const armnn::DataLayout layout)
{
return CompareDepthwiseConvolution2dTestImpl<armnn::DataType::Float32>(
workloadFactory, memoryManager, refWorkloadFactory, layout);
}
LayerTestResult<uint8_t, 4> CompareDepthwiseConvolution2dUint8Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
armnn::IWorkloadFactory& refWorkloadFactory,
const armnn::DataLayout layout)
{
return CompareDepthwiseConvolution2dTestImpl<armnn::DataType::QAsymmU8>(
workloadFactory, memoryManager, refWorkloadFactory, layout);
}